A clinical investigation of constructing a diagnostic model for sepsis-induced coagulopathy utilizing data-independent acquisition proteomics
10.3760/cma.j.cn121090-20241219-00579
- VernacularTitle:基于DIA蛋白组学构建脓毒症性凝血病诊断模型的临床研究
- Author:
Qi CHEN
1
;
Jingchun SONG
1
;
Xiaolei WAN
1
;
Junjie ZENG
1
;
Xiaomin SONG
1
;
Lincui ZHONG
1
;
Longping HE
1
Author Information
1. 解放军联勤保障部队第九〇八医院重症医学科/南昌大学附属长城医院重症医学科，南昌 330002
- Publication Type:Journal Article
- Keywords:
Sepsis-induced Coagulopathy;
Proteomics;
Diagnostic Model;
Nomogram;
Angiogenin
- From:
Chinese Journal of Hematology
2025;46(1):45-52
- CountryChina
- Language:Chinese
-
Abstract:
Objective:This study used data-independent acquisition (DIA) proteomics to analyze plasma protein expression in sepsis-induced coagulopathy (SIC), identify key biomarkers, and develop a diagnostic model.Methods:This prospective study included 46 adult sepsis patients from the intensive care unit. Patients were categorized into a general sepsis group ( n=26) and an SIC group ( n=20) based on established SIC criteria. Plasma samples underwent proteomic and bioinformatics analyses to identify differentially expressed protein (DEP) using LASSO regression and Random Forest. A diagnostic model was constructed and assessed via receiver operating characteristic (ROC) curve analysis. Results:The baseline data revealed that SIC patients exhibited longer prothrombin times, lower platelet counts, and higher D-dimer, fibrin degradation products, blood lactate, SOFA scores, and APACHE Ⅱ scores compared with general sepsis patients ( P<0.05). DIA proteomics identified 2 637 proteins, with 240 DEP meeting the criteria (fold change >1.5, P<0.05), including 81 upregulated and 159 downregulated DEP. Subcellular localization analysis revealed that DEPs were predominantly extracellular and nuclear. Gene ontology (GO) annotation showed that DEP were mainly involved in cellular physiology, biological regulation, and stress response processes in biological processes. Domain annotation revealed a predominance of immunoglobulin V regions in DEP, which are crucial for antigen recognition and binding. KEGG enrichment analysis showed significant enrichment of DEP in pathways related to natural killer cell-mediated cytotoxicity, glycosylphosphatidylinositol anchor biosynthesis, tumor necrosis factor signaling, and NF-κB signaling. LASSO regression identified angiogenin and C-type lectin domain family 10 member A as key DEP. The SIC diagnostic nomogram showed an area under the curve of 0.896, with 0.731 specificity and 0.900 sensitivity. Conclusion:The nomogram incorporating angiogenin and C-type lectin domain family 10 member A provides an accurate tool for SIC diagnosis.
